{
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   "source": [
    "<a href=\"https://www.bigdatauniversity.com\"><img src=\"https://ibm.box.com/shared/static/cw2c7r3o20w9zn8gkecaeyjhgw3xdgbj.png\" width=\"400\" align=\"center\"></a>\n",
    "\n",
    "<h1><center>K-Means Clustering</center></h1>"
   ]
  },
  {
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   "source": [
    "## Introduction\n",
    "\n",
    "There are many models for **clustering** out there. In this notebook, we will be presenting the model that is considered one of the simplest models amongst them. Despite its simplicity, the **K-means** is vastly used for clustering in many data science applications, especially useful if you need to quickly discover insights from **unlabeled data**. In this notebook, you will learn how to use k-Means for customer segmentation.\n",
    "\n",
    "Some real-world applications of k-means:\n",
    "- Customer segmentation\n",
    "- Understand what the visitors of a website are trying to accomplish\n",
    "- Pattern recognition\n",
    "- Machine learning\n",
    "- Data compression\n",
    "\n",
    "\n",
    "In this notebook we practice k-means clustering with 2 examples:\n",
    "- k-means on a random generated dataset\n",
    "- Using k-means for customer segmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Table of contents</h1>\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "    <ul>\n",
    "        <li><a href=\"#random_generated_dataset\">k-Means on a randomly generated dataset</a></li>\n",
    "            <ol>\n",
    "                <li><a href=\"#setting_up_K_means\">Setting up K-Means</a></li>\n",
    "                <li><a href=\"#creating_visual_plot\">Creating the Visual Plot</a></li>\n",
    "            </ol>\n",
    "        <li><a href=\"#customer_segmentation_K_means\">Customer Segmentation with K-Means</a></li>\n",
    "            <ol>\n",
    "                <li><a href=\"#pre_processing\">Pre-processing</a></li>\n",
    "                <li><a href=\"#modeling\">Modeling</a></li>\n",
    "                <li><a href=\"#insights\">Insights</a></li>\n",
    "            </ol>\n",
    "    </ul>\n",
    "</div>\n",
    "<br>\n",
    "<hr>"
   ]
  },
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   "source": [
    "### Import libraries\n",
    "Lets first import the required libraries.\n",
    "Also run <b> %matplotlib inline </b> since we will be plotting in this section."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "import random \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "from sklearn.cluster import KMeans \n",
    "from sklearn.datasets.samples_generator import make_blobs \n",
    "%matplotlib inline"
   ]
  },
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       "\u001b[0;31mInit signature:\u001b[0m\n",
       "\u001b[0mKMeans\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mn_clusters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0minit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'k-means++'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mn_init\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmax_iter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mtol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.0001\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mprecompute_distances\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'auto'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcopy_x\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0malgorithm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'auto'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m     \n",
       "K-Means clustering\n",
       "\n",
       "Read more in the :ref:`User Guide <k_means>`.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "\n",
       "n_clusters : int, optional, default: 8\n",
       "    The number of clusters to form as well as the number of\n",
       "    centroids to generate.\n",
       "\n",
       "init : {'k-means++', 'random' or an ndarray}\n",
       "    Method for initialization, defaults to 'k-means++':\n",
       "\n",
       "    'k-means++' : selects initial cluster centers for k-mean\n",
       "    clustering in a smart way to speed up convergence. See section\n",
       "    Notes in k_init for more details.\n",
       "\n",
       "    'random': choose k observations (rows) at random from data for\n",
       "    the initial centroids.\n",
       "\n",
       "    If an ndarray is passed, it should be of shape (n_clusters, n_features)\n",
       "    and gives the initial centers.\n",
       "\n",
       "n_init : int, default: 10\n",
       "    Number of time the k-means algorithm will be run with different\n",
       "    centroid seeds. The final results will be the best output of\n",
       "    n_init consecutive runs in terms of inertia.\n",
       "\n",
       "max_iter : int, default: 300\n",
       "    Maximum number of iterations of the k-means algorithm for a\n",
       "    single run.\n",
       "\n",
       "tol : float, default: 1e-4\n",
       "    Relative tolerance with regards to inertia to declare convergence\n",
       "\n",
       "precompute_distances : {'auto', True, False}\n",
       "    Precompute distances (faster but takes more memory).\n",
       "\n",
       "    'auto' : do not precompute distances if n_samples * n_clusters > 12\n",
       "    million. This corresponds to about 100MB overhead per job using\n",
       "    double precision.\n",
       "\n",
       "    True : always precompute distances\n",
       "\n",
       "    False : never precompute distances\n",
       "\n",
       "verbose : int, default 0\n",
       "    Verbosity mode.\n",
       "\n",
       "random_state : int, RandomState instance or None (default)\n",
       "    Determines random number generation for centroid initialization. Use\n",
       "    an int to make the randomness deterministic.\n",
       "    See :term:`Glossary <random_state>`.\n",
       "\n",
       "copy_x : boolean, optional\n",
       "    When pre-computing distances it is more numerically accurate to center\n",
       "    the data first.  If copy_x is True (default), then the original data is\n",
       "    not modified, ensuring X is C-contiguous.  If False, the original data\n",
       "    is modified, and put back before the function returns, but small\n",
       "    numerical differences may be introduced by subtracting and then adding\n",
       "    the data mean, in this case it will also not ensure that data is\n",
       "    C-contiguous which may cause a significant slowdown.\n",
       "\n",
       "n_jobs : int or None, optional (default=None)\n",
       "    The number of jobs to use for the computation. This works by computing\n",
       "    each of the n_init runs in parallel.\n",
       "\n",
       "    ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\n",
       "    ``-1`` means using all processors. See :term:`Glossary <n_jobs>`\n",
       "    for more details.\n",
       "\n",
       "algorithm : \"auto\", \"full\" or \"elkan\", default=\"auto\"\n",
       "    K-means algorithm to use. The classical EM-style algorithm is \"full\".\n",
       "    The \"elkan\" variation is more efficient by using the triangle\n",
       "    inequality, but currently doesn't support sparse data. \"auto\" chooses\n",
       "    \"elkan\" for dense data and \"full\" for sparse data.\n",
       "\n",
       "Attributes\n",
       "----------\n",
       "cluster_centers_ : array, [n_clusters, n_features]\n",
       "    Coordinates of cluster centers. If the algorithm stops before fully\n",
       "    converging (see ``tol`` and ``max_iter``), these will not be\n",
       "    consistent with ``labels_``.\n",
       "\n",
       "labels_ :\n",
       "    Labels of each point\n",
       "\n",
       "inertia_ : float\n",
       "    Sum of squared distances of samples to their closest cluster center.\n",
       "\n",
       "n_iter_ : int\n",
       "    Number of iterations run.\n",
       "\n",
       "Examples\n",
       "--------\n",
       "\n",
       ">>> from sklearn.cluster import KMeans\n",
       ">>> import numpy as np\n",
       ">>> X = np.array([[1, 2], [1, 4], [1, 0],\n",
       "...               [4, 2], [4, 4], [4, 0]])\n",
       ">>> kmeans = KMeans(n_clusters=2, random_state=0).fit(X)\n",
       ">>> kmeans.labels_\n",
       "array([0, 0, 0, 1, 1, 1], dtype=int32)\n",
       ">>> kmeans.predict([[0, 0], [4, 4]])\n",
       "array([0, 1], dtype=int32)\n",
       ">>> kmeans.cluster_centers_\n",
       "array([[1., 2.],\n",
       "       [4., 2.]])\n",
       "\n",
       "See also\n",
       "--------\n",
       "\n",
       "MiniBatchKMeans\n",
       "    Alternative online implementation that does incremental updates\n",
       "    of the centers positions using mini-batches.\n",
       "    For large scale learning (say n_samples > 10k) MiniBatchKMeans is\n",
       "    probably much faster than the default batch implementation.\n",
       "\n",
       "Notes\n",
       "------\n",
       "The k-means problem is solved using either Lloyd's or Elkan's algorithm.\n",
       "\n",
       "The average complexity is given by O(k n T), were n is the number of\n",
       "samples and T is the number of iteration.\n",
       "\n",
       "The worst case complexity is given by O(n^(k+2/p)) with\n",
       "n = n_samples, p = n_features. (D. Arthur and S. Vassilvitskii,\n",
       "'How slow is the k-means method?' SoCG2006)\n",
       "\n",
       "In practice, the k-means algorithm is very fast (one of the fastest\n",
       "clustering algorithms available), but it falls in local minima. That's why\n",
       "it can be useful to restart it several times.\n",
       "\n",
       "If the algorithm stops before fully converging (because of ``tol`` or\n",
       "``max_iter``), ``labels_`` and ``cluster_centers_`` will not be consistent,\n",
       "i.e. the ``cluster_centers_`` will not be the means of the points in each\n",
       "cluster. Also, the estimator will reassign ``labels_`` after the last\n",
       "iteration to make ``labels_`` consistent with ``predict`` on the training\n",
       "set.\n",
       "\u001b[0;31mFile:\u001b[0m           ~/conda/envs/python/lib/python3.6/site-packages/sklearn/cluster/k_means_.py\n",
       "\u001b[0;31mType:\u001b[0m           type\n",
       "\u001b[0;31mSubclasses:\u001b[0m     MiniBatchKMeans\n"
      ]
     },
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    }
   ],
   "source": [
    "KMeans?"
   ]
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       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mmake_blobs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mn_samples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mn_features\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcenters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcluster_std\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcenter_box\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m10.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Generate isotropic Gaussian blobs for clustering.\n",
       "\n",
       "Read more in the :ref:`User Guide <sample_generators>`.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "n_samples : int or array-like, optional (default=100)\n",
       "    If int, it is the total number of points equally divided among\n",
       "    clusters.\n",
       "    If array-like, each element of the sequence indicates\n",
       "    the number of samples per cluster.\n",
       "\n",
       "n_features : int, optional (default=2)\n",
       "    The number of features for each sample.\n",
       "\n",
       "centers : int or array of shape [n_centers, n_features], optional\n",
       "    (default=None)\n",
       "    The number of centers to generate, or the fixed center locations.\n",
       "    If n_samples is an int and centers is None, 3 centers are generated.\n",
       "    If n_samples is array-like, centers must be\n",
       "    either None or an array of length equal to the length of n_samples.\n",
       "\n",
       "cluster_std : float or sequence of floats, optional (default=1.0)\n",
       "    The standard deviation of the clusters.\n",
       "\n",
       "center_box : pair of floats (min, max), optional (default=(-10.0, 10.0))\n",
       "    The bounding box for each cluster center when centers are\n",
       "    generated at random.\n",
       "\n",
       "shuffle : boolean, optional (default=True)\n",
       "    Shuffle the samples.\n",
       "\n",
       "random_state : int, RandomState instance or None (default)\n",
       "    Determines random number generation for dataset creation. Pass an int\n",
       "    for reproducible output across multiple function calls.\n",
       "    See :term:`Glossary <random_state>`.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "X : array of shape [n_samples, n_features]\n",
       "    The generated samples.\n",
       "\n",
       "y : array of shape [n_samples]\n",
       "    The integer labels for cluster membership of each sample.\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> from sklearn.datasets.samples_generator import make_blobs\n",
       ">>> X, y = make_blobs(n_samples=10, centers=3, n_features=2,\n",
       "...                   random_state=0)\n",
       ">>> print(X.shape)\n",
       "(10, 2)\n",
       ">>> y\n",
       "array([0, 0, 1, 0, 2, 2, 2, 1, 1, 0])\n",
       ">>> X, y = make_blobs(n_samples=[3, 3, 4], centers=None, n_features=2,\n",
       "...                   random_state=0)\n",
       ">>> print(X.shape)\n",
       "(10, 2)\n",
       ">>> y\n",
       "array([0, 1, 2, 0, 2, 2, 2, 1, 1, 0])\n",
       "\n",
       "See also\n",
       "--------\n",
       "make_classification: a more intricate variant\n",
       "\u001b[0;31mFile:\u001b[0m      ~/conda/envs/python/lib/python3.6/site-packages/sklearn/datasets/samples_generator.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
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   ],
   "source": [
    "make_blobs?"
   ]
  },
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   "source": [
    "<h1 id=\"random_generated_dataset\">k-Means on a randomly generated dataset</h1>\n",
    "Lets create our own dataset for this lab!\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "First we need to set up a random seed. Use <b>numpy's random.seed()</b> function, where the seed will be set to <b>0</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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   "source": [
    "np.random.seed(0)"
   ]
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   "source": [
    "Next we will be making <i> random clusters </i> of points by using the <b> make_blobs </b> class. The <b> make_blobs </b> class can take in many inputs, but we will be using these specific ones. <br> <br>\n",
    "<b> <u> Input </u> </b>\n",
    "<ul>\n",
    "    <li> <b>n_samples</b>: The total number of points equally divided among clusters. </li>\n",
    "    <ul> <li> Value will be: 5000 </li> </ul>\n",
    "    <li> <b>centers</b>: The number of centers to generate, or the fixed center locations. </li>\n",
    "    <ul> <li> Value will be: [[4, 4], [-2, -1], [2, -3],[1,1]] </li> </ul>\n",
    "    <li> <b>cluster_std</b>: The standard deviation of the clusters. </li>\n",
    "    <ul> <li> Value will be: 0.9 </li> </ul>\n",
    "</ul>\n",
    "<br>\n",
    "<b> <u> Output </u> </b>\n",
    "<ul>\n",
    "    <li> <b>X</b>: Array of shape [n_samples, n_features]. (Feature Matrix)</li>\n",
    "    <ul> <li> The generated samples. </li> </ul> \n",
    "    <li> <b>y</b>: Array of shape [n_samples]. (Response Vector)</li>\n",
    "    <ul> <li> The integer labels for cluster membership of each sample. </li> </ul>\n",
    "</ul>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "X, y = make_blobs(n_samples=5000, centers=[[4,4], [-2, -1], [2, -3], [1, 1]], cluster_std=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Display the scatter plot of the randomly generated data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f12c9d224e0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:, 0], X[:, 1], marker='.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<h2 id=\"setting_up_K_means\">Setting up K-Means</h2>\n",
    "Now that we have our random data, let's set up our K-Means Clustering."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The KMeans class has many parameters that can be used, but we will be using these three:\n",
    "<ul>\n",
    "    <li> <b>init</b>: Initialization method of the centroids. </li>\n",
    "    <ul>\n",
    "        <li> Value will be: \"k-means++\" </li>\n",
    "        <li> k-means++: Selects initial cluster centers for k-mean clustering in a smart way to speed up convergence.</li>\n",
    "    </ul>\n",
    "    <li> <b>n_clusters</b>: The number of clusters to form as well as the number of centroids to generate. </li>\n",
    "    <ul> <li> Value will be: 4 (since we have 4 centers)</li> </ul>\n",
    "    <li> <b>n_init</b>: Number of time the k-means algorithm will be run with different centroid seeds. The final results will be the best output of n_init consecutive runs in terms of inertia. </li>\n",
    "    <ul> <li> Value will be: 12 </li> </ul>\n",
    "</ul>\n",
    "\n",
    "Initialize KMeans with these parameters, where the output parameter is called <b>k_means</b>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "k_means = KMeans(init = \"k-means++\", n_clusters = 4, n_init = 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Now let's fit the KMeans model with the feature matrix we created above, <b> X </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=4, n_init=12, n_jobs=None, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_means.fit(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Now let's grab the labels for each point in the model using KMeans' <b> .labels\\_ </b> attribute and save it as <b> k_means_labels </b> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 3, 3, ..., 1, 0, 0], dtype=int32)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_means_labels = k_means.labels_\n",
    "k_means_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We will also get the coordinates of the cluster centers using KMeans' <b> .cluster&#95;centers&#95; </b> and save it as <b> k_means_cluster_centers </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.03743147, -0.99782524],\n",
       "       [ 3.97334234,  3.98758687],\n",
       "       [ 0.96900523,  0.98370298],\n",
       "       [ 1.99741008, -3.01666822]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_means_cluster_centers = k_means.cluster_centers_\n",
    "k_means_cluster_centers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<h2 id=\"creating_visual_plot\">Creating the Visual Plot</h2>\n",
    "So now that we have the random data generated and the KMeans model initialized, let's plot them and see what it looks like!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Please read through the code and comments to understand how to plot the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the plot with the specified dimensions.\n",
    "fig = plt.figure(figsize=(6, 4))\n",
    "\n",
    "# Colors uses a color map, which will produce an array of colors based on\n",
    "# the number of labels there are. We use set(k_means_labels) to get the\n",
    "# unique labels.\n",
    "colors = plt.cm.Spectral(np.linspace(0, 1, len(set(k_means_labels))))\n",
    "\n",
    "# Create a plot\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "# For loop that plots the data points and centroids.\n",
    "# k will range from 0-3, which will match the possible clusters that each\n",
    "# data point is in.\n",
    "for k, col in zip(range(len([[4,4], [-2, -1], [2, -3], [1, 1]])), colors):\n",
    "\n",
    "    # Create a list of all data points, where the data poitns that are \n",
    "    # in the cluster (ex. cluster 0) are labeled as true, else they are\n",
    "    # labeled as false.\n",
    "    my_members = (k_means_labels == k)\n",
    "    \n",
    "    # Define the centroid, or cluster center.\n",
    "    cluster_center = k_means_cluster_centers[k]\n",
    "    \n",
    "    # Plots the datapoints with color col.\n",
    "    ax.plot(X[my_members, 0], X[my_members, 1], 'w', markerfacecolor=col, marker='.')\n",
    "    \n",
    "    # Plots the centroids with specified color, but with a darker outline\n",
    "    ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,  markeredgecolor='k', markersize=6)\n",
    "\n",
    "# Title of the plot\n",
    "ax.set_title('KMeans')\n",
    "\n",
    "# Remove x-axis ticks\n",
    "ax.set_xticks(())\n",
    "\n",
    "# Remove y-axis ticks\n",
    "ax.set_yticks(())\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Practice\n",
    "Try to cluster the above dataset into 3 clusters.  \n",
    "Notice: do not generate data again, use the same dataset as above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# write your code here\n",
    "k_means3 = KMeans(init = \"k-means++\", n_clusters = 3, n_init = 12)\n",
    "k_means3.fit(X)\n",
    "fig = plt.figure(figsize=(6, 4))\n",
    "colors = plt.cm.Spectral(np.linspace(0, 1, len(set(k_means3.labels_))))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "for k, col in zip(range(len(k_means3.cluster_centers_)), colors):\n",
    "    my_members = (k_means3.labels_ == k)\n",
    "    cluster_center = k_means3.cluster_centers_[k]\n",
    "    ax.plot(X[my_members, 0], X[my_members, 1], 'w', markerfacecolor=col, marker='.')\n",
    "    ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,  markeredgecolor='k', markersize=6)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "\n",
    "<!-- Your answer is below:\n",
    "\n",
    "k_means3 = KMeans(init = \"k-means++\", n_clusters = 3, n_init = 12)\n",
    "k_means3.fit(X)\n",
    "fig = plt.figure(figsize=(6, 4))\n",
    "colors = plt.cm.Spectral(np.linspace(0, 1, len(set(k_means3.labels_))))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "for k, col in zip(range(len(k_means3.cluster_centers_)), colors):\n",
    "    my_members = (k_means3.labels_ == k)\n",
    "    cluster_center = k_means3.cluster_centers_[k]\n",
    "    ax.plot(X[my_members, 0], X[my_members, 1], 'w', markerfacecolor=col, marker='.')\n",
    "    ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,  markeredgecolor='k', markersize=6)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<h1 id=\"customer_segmentation_K_means\">Customer Segmentation with K-Means</h1>\n",
    "Imagine that you have a customer dataset, and you need to apply customer segmentation on this historical data.\n",
    "Customer segmentation is the practice of partitioning a customer base into groups of individuals that have similar characteristics. It is a significant strategy as a business can target these specific groups of customers and effectively allocate marketing resources. For example, one group might contain customers who are high-profit and low-risk, that is, more likely to purchase products, or subscribe for a service. A business task is to retaining those customers. Another group might include customers from non-profit organizations. And so on.\n",
    "\n",
    "Lets download the dataset. To download the data, we will use **`!wget`** to download it from IBM Object Storage.  \n",
    "__Did you know?__ When it comes to Machine Learning, you will likely be working with large datasets. As a business, where can you host your data? IBM is offering a unique opportunity for businesses, with 10 Tb of IBM Cloud Object Storage: [Sign up now for free](http://cocl.us/ML0101EN-IBM-Offer-CC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2019-10-05 01:08:11--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/Cust_Segmentation.csv\n",
      "Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.193\n",
      "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.193|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 34276 (33K) [text/csv]\n",
      "Saving to: ‘Cust_Segmentation.csv’\n",
      "\n",
      "Cust_Segmentation.c 100%[===================>]  33.47K  --.-KB/s    in 0.02s   \n",
      "\n",
      "2019-10-05 01:08:12 (1.54 MB/s) - ‘Cust_Segmentation.csv’ saved [34276/34276]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O Cust_Segmentation.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/Cust_Segmentation.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Load Data From CSV File  \n",
    "Before you can work with the data, you must use the URL to get the Cust_Segmentation.csv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer Id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Edu</th>\n",
       "      <th>Years Employed</th>\n",
       "      <th>Income</th>\n",
       "      <th>Card Debt</th>\n",
       "      <th>Other Debt</th>\n",
       "      <th>Defaulted</th>\n",
       "      <th>Address</th>\n",
       "      <th>DebtIncomeRatio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>0.124</td>\n",
       "      <td>1.073</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NBA001</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>100</td>\n",
       "      <td>4.582</td>\n",
       "      <td>8.218</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NBA021</td>\n",
       "      <td>12.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>57</td>\n",
       "      <td>6.111</td>\n",
       "      <td>5.802</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NBA013</td>\n",
       "      <td>20.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.516</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NBA009</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>253</td>\n",
       "      <td>9.308</td>\n",
       "      <td>8.908</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NBA008</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer Id  Age  Edu  Years Employed  Income  Card Debt  Other Debt  \\\n",
       "0            1   41    2               6      19      0.124       1.073   \n",
       "1            2   47    1              26     100      4.582       8.218   \n",
       "2            3   33    2              10      57      6.111       5.802   \n",
       "3            4   29    2               4      19      0.681       0.516   \n",
       "4            5   47    1              31     253      9.308       8.908   \n",
       "\n",
       "   Defaulted Address  DebtIncomeRatio  \n",
       "0        0.0  NBA001              6.3  \n",
       "1        0.0  NBA021             12.8  \n",
       "2        1.0  NBA013             20.9  \n",
       "3        0.0  NBA009              6.3  \n",
       "4        0.0  NBA008              7.2  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "cust_df = pd.read_csv(\"Cust_Segmentation.csv\")\n",
    "cust_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"pre_processing\">Pre-processing</h2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
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   },
   "source": [
    "As you can see, __Address__ in this dataset is a categorical variable. k-means algorithm isn't directly applicable to categorical variables because Euclidean distance function isn't really meaningful for discrete variables. So, lets drop this feature and run clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "button": false,
    "collapsed": false,
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer Id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Edu</th>\n",
       "      <th>Years Employed</th>\n",
       "      <th>Income</th>\n",
       "      <th>Card Debt</th>\n",
       "      <th>Other Debt</th>\n",
       "      <th>Defaulted</th>\n",
       "      <th>DebtIncomeRatio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>0.124</td>\n",
       "      <td>1.073</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>100</td>\n",
       "      <td>4.582</td>\n",
       "      <td>8.218</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>57</td>\n",
       "      <td>6.111</td>\n",
       "      <td>5.802</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
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       "      <td>2</td>\n",
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       "      <td>19</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.516</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>253</td>\n",
       "      <td>9.308</td>\n",
       "      <td>8.908</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer Id  Age  Edu  Years Employed  Income  Card Debt  Other Debt  \\\n",
       "0            1   41    2               6      19      0.124       1.073   \n",
       "1            2   47    1              26     100      4.582       8.218   \n",
       "2            3   33    2              10      57      6.111       5.802   \n",
       "3            4   29    2               4      19      0.681       0.516   \n",
       "4            5   47    1              31     253      9.308       8.908   \n",
       "\n",
       "   Defaulted  DebtIncomeRatio  \n",
       "0        0.0              6.3  \n",
       "1        0.0             12.8  \n",
       "2        1.0             20.9  \n",
       "3        0.0              6.3  \n",
       "4        0.0              7.2  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = cust_df.drop('Address', axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Normalizing over the standard deviation\n",
    "Now let's normalize the dataset. But why do we need normalization in the first place? Normalization is a statistical method that helps mathematical-based algorithms to interpret features with different magnitudes and distributions equally. We use __StandardScaler()__ to normalize our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.74291541,  0.31212243, -0.37878978, ..., -0.59048916,\n",
       "        -0.52379654, -0.57652509],\n",
       "       [ 1.48949049, -0.76634938,  2.5737211 , ...,  1.51296181,\n",
       "        -0.52379654,  0.39138677],\n",
       "       [-0.25251804,  0.31212243,  0.2117124 , ...,  0.80170393,\n",
       "         1.90913822,  1.59755385],\n",
       "       ...,\n",
       "       [-1.24795149,  2.46906604, -1.26454304, ...,  0.03863257,\n",
       "         1.90913822,  3.45892281],\n",
       "       [-0.37694723, -0.76634938,  0.50696349, ..., -0.70147601,\n",
       "        -0.52379654, -1.08281745],\n",
       "       [ 2.1116364 , -0.76634938,  1.09746566, ...,  0.16463355,\n",
       "        -0.52379654, -0.2340332 ]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "X = df.values[:,1:]\n",
    "X = np.nan_to_num(X)\n",
    "Clus_dataSet = StandardScaler().fit_transform(X)\n",
    "Clus_dataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mInit signature:\u001b[0m \u001b[0mStandardScaler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwith_mean\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwith_std\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m     \n",
       "Standardize features by removing the mean and scaling to unit variance\n",
       "\n",
       "The standard score of a sample `x` is calculated as:\n",
       "\n",
       "    z = (x - u) / s\n",
       "\n",
       "where `u` is the mean of the training samples or zero if `with_mean=False`,\n",
       "and `s` is the standard deviation of the training samples or one if\n",
       "`with_std=False`.\n",
       "\n",
       "Centering and scaling happen independently on each feature by computing\n",
       "the relevant statistics on the samples in the training set. Mean and\n",
       "standard deviation are then stored to be used on later data using the\n",
       "`transform` method.\n",
       "\n",
       "Standardization of a dataset is a common requirement for many\n",
       "machine learning estimators: they might behave badly if the\n",
       "individual features do not more or less look like standard normally\n",
       "distributed data (e.g. Gaussian with 0 mean and unit variance).\n",
       "\n",
       "For instance many elements used in the objective function of\n",
       "a learning algorithm (such as the RBF kernel of Support Vector\n",
       "Machines or the L1 and L2 regularizers of linear models) assume that\n",
       "all features are centered around 0 and have variance in the same\n",
       "order. If a feature has a variance that is orders of magnitude larger\n",
       "that others, it might dominate the objective function and make the\n",
       "estimator unable to learn from other features correctly as expected.\n",
       "\n",
       "This scaler can also be applied to sparse CSR or CSC matrices by passing\n",
       "`with_mean=False` to avoid breaking the sparsity structure of the data.\n",
       "\n",
       "Read more in the :ref:`User Guide <preprocessing_scaler>`.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "copy : boolean, optional, default True\n",
       "    If False, try to avoid a copy and do inplace scaling instead.\n",
       "    This is not guaranteed to always work inplace; e.g. if the data is\n",
       "    not a NumPy array or scipy.sparse CSR matrix, a copy may still be\n",
       "    returned.\n",
       "\n",
       "with_mean : boolean, True by default\n",
       "    If True, center the data before scaling.\n",
       "    This does not work (and will raise an exception) when attempted on\n",
       "    sparse matrices, because centering them entails building a dense\n",
       "    matrix which in common use cases is likely to be too large to fit in\n",
       "    memory.\n",
       "\n",
       "with_std : boolean, True by default\n",
       "    If True, scale the data to unit variance (or equivalently,\n",
       "    unit standard deviation).\n",
       "\n",
       "Attributes\n",
       "----------\n",
       "scale_ : ndarray or None, shape (n_features,)\n",
       "    Per feature relative scaling of the data. This is calculated using\n",
       "    `np.sqrt(var_)`. Equal to ``None`` when ``with_std=False``.\n",
       "\n",
       "    .. versionadded:: 0.17\n",
       "       *scale_*\n",
       "\n",
       "mean_ : ndarray or None, shape (n_features,)\n",
       "    The mean value for each feature in the training set.\n",
       "    Equal to ``None`` when ``with_mean=False``.\n",
       "\n",
       "var_ : ndarray or None, shape (n_features,)\n",
       "    The variance for each feature in the training set. Used to compute\n",
       "    `scale_`. Equal to ``None`` when ``with_std=False``.\n",
       "\n",
       "n_samples_seen_ : int or array, shape (n_features,)\n",
       "    The number of samples processed by the estimator for each feature.\n",
       "    If there are not missing samples, the ``n_samples_seen`` will be an\n",
       "    integer, otherwise it will be an array.\n",
       "    Will be reset on new calls to fit, but increments across\n",
       "    ``partial_fit`` calls.\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> from sklearn.preprocessing import StandardScaler\n",
       ">>> data = [[0, 0], [0, 0], [1, 1], [1, 1]]\n",
       ">>> scaler = StandardScaler()\n",
       ">>> print(scaler.fit(data))\n",
       "StandardScaler(copy=True, with_mean=True, with_std=True)\n",
       ">>> print(scaler.mean_)\n",
       "[0.5 0.5]\n",
       ">>> print(scaler.transform(data))\n",
       "[[-1. -1.]\n",
       " [-1. -1.]\n",
       " [ 1.  1.]\n",
       " [ 1.  1.]]\n",
       ">>> print(scaler.transform([[2, 2]]))\n",
       "[[3. 3.]]\n",
       "\n",
       "See also\n",
       "--------\n",
       "scale: Equivalent function without the estimator API.\n",
       "\n",
       ":class:`sklearn.decomposition.PCA`\n",
       "    Further removes the linear correlation across features with 'whiten=True'.\n",
       "\n",
       "Notes\n",
       "-----\n",
       "NaNs are treated as missing values: disregarded in fit, and maintained in\n",
       "transform.\n",
       "\n",
       "For a comparison of the different scalers, transformers, and normalizers,\n",
       "see :ref:`examples/preprocessing/plot_all_scaling.py\n",
       "<sphx_glr_auto_examples_preprocessing_plot_all_scaling.py>`.\n",
       "\u001b[0;31mFile:\u001b[0m           ~/conda/envs/python/lib/python3.6/site-packages/sklearn/preprocessing/data.py\n",
       "\u001b[0;31mType:\u001b[0m           type\n",
       "\u001b[0;31mSubclasses:\u001b[0m     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "StandardScaler?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"modeling\">Modeling</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
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   },
   "source": [
    "In our example (if we didn't have access to the k-means algorithm), it would be the same as guessing that each customer group would have certain age, income, education, etc, with multiple tests and experiments. However, using the K-means clustering we can do all this process much easier.\n",
    "\n",
    "Lets apply k-means on our dataset, and take look at cluster labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      " 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 2]\n"
     ]
    }
   ],
   "source": [
    "clusterNum = 3\n",
    "k_means = KMeans(init = \"k-means++\", n_clusters = clusterNum, n_init = 12)\n",
    "k_means.fit(X)\n",
    "labels = k_means.labels_\n",
    "print(labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "deletable": true,
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   },
   "source": [
    "<h2 id=\"insights\">Insights</h2>\n",
    "We assign the labels to each row in dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
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   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer Id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Edu</th>\n",
       "      <th>Years Employed</th>\n",
       "      <th>Income</th>\n",
       "      <th>Card Debt</th>\n",
       "      <th>Other Debt</th>\n",
       "      <th>Defaulted</th>\n",
       "      <th>DebtIncomeRatio</th>\n",
       "      <th>Clus_km</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>0.124</td>\n",
       "      <td>1.073</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>100</td>\n",
       "      <td>4.582</td>\n",
       "      <td>8.218</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>57</td>\n",
       "      <td>6.111</td>\n",
       "      <td>5.802</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.516</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>253</td>\n",
       "      <td>9.308</td>\n",
       "      <td>8.908</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer Id  Age  Edu  Years Employed  Income  Card Debt  Other Debt  \\\n",
       "0            1   41    2               6      19      0.124       1.073   \n",
       "1            2   47    1              26     100      4.582       8.218   \n",
       "2            3   33    2              10      57      6.111       5.802   \n",
       "3            4   29    2               4      19      0.681       0.516   \n",
       "4            5   47    1              31     253      9.308       8.908   \n",
       "\n",
       "   Defaulted  DebtIncomeRatio  Clus_km  \n",
       "0        0.0              6.3        0  \n",
       "1        0.0             12.8        2  \n",
       "2        1.0             20.9        0  \n",
       "3        0.0              6.3        0  \n",
       "4        0.0              7.2        1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Clus_km\"] = labels\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can easily check the centroid values by averaging the features in each cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer Id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Edu</th>\n",
       "      <th>Years Employed</th>\n",
       "      <th>Income</th>\n",
       "      <th>Card Debt</th>\n",
       "      <th>Other Debt</th>\n",
       "      <th>Defaulted</th>\n",
       "      <th>DebtIncomeRatio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clus_km</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>432.006154</td>\n",
       "      <td>32.967692</td>\n",
       "      <td>1.613846</td>\n",
       "      <td>6.389231</td>\n",
       "      <td>31.204615</td>\n",
       "      <td>1.032711</td>\n",
       "      <td>2.108345</td>\n",
       "      <td>0.284658</td>\n",
       "      <td>10.095385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>410.166667</td>\n",
       "      <td>45.388889</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>19.555556</td>\n",
       "      <td>227.166667</td>\n",
       "      <td>5.678444</td>\n",
       "      <td>10.907167</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>7.322222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>403.780220</td>\n",
       "      <td>41.368132</td>\n",
       "      <td>1.961538</td>\n",
       "      <td>15.252747</td>\n",
       "      <td>84.076923</td>\n",
       "      <td>3.114412</td>\n",
       "      <td>5.770352</td>\n",
       "      <td>0.172414</td>\n",
       "      <td>10.725824</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Customer Id        Age       Edu  Years Employed      Income  \\\n",
       "Clus_km                                                                 \n",
       "0         432.006154  32.967692  1.613846        6.389231   31.204615   \n",
       "1         410.166667  45.388889  2.666667       19.555556  227.166667   \n",
       "2         403.780220  41.368132  1.961538       15.252747   84.076923   \n",
       "\n",
       "         Card Debt  Other Debt  Defaulted  DebtIncomeRatio  \n",
       "Clus_km                                                     \n",
       "0         1.032711    2.108345   0.284658        10.095385  \n",
       "1         5.678444   10.907167   0.285714         7.322222  \n",
       "2         3.114412    5.770352   0.172414        10.725824  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Clus_km').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, lets look at the distribution of customers based on their age and income:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "area = np.pi * ( X[:, 1])**2  \n",
    "plt.scatter(X[:, 0], X[:, 3], s=area, c=labels.astype(np.float), alpha=0.5)\n",
    "plt.xlabel('Age', fontsize=18)\n",
    "plt.ylabel('Income', fontsize=16)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x7f12bda03320>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D \n",
    "fig = plt.figure(1, figsize=(8, 6))\n",
    "plt.clf()\n",
    "ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)\n",
    "\n",
    "plt.cla()\n",
    "# plt.ylabel('Age', fontsize=18)\n",
    "# plt.xlabel('Income', fontsize=16)\n",
    "# plt.zlabel('Education', fontsize=16)\n",
    "ax.set_xlabel('Education')\n",
    "ax.set_ylabel('Age')\n",
    "ax.set_zlabel('Income')\n",
    "\n",
    "ax.scatter(X[:, 1], X[:, 0], X[:, 3], c= labels.astype(np.float))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mInit signature:\u001b[0m\n",
       "\u001b[0mAxes3D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mrect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mazim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m60\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0melev\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mzscale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0msharez\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mproj_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'persp'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m      3D axes object.\n",
       "\u001b[0;31mInit docstring:\u001b[0m\n",
       "Parameters\n",
       "----------\n",
       "fig : Figure\n",
       "    The parent figure.\n",
       "rect : (float, float, float, float)\n",
       "    The ``(left, bottom, width, height)`` axes position.\n",
       "azim : float, optional\n",
       "    Azimuthal viewing angle, defaults to -60.\n",
       "elev : float, optional\n",
       "    Elevation viewing angle, defaults to 30.\n",
       "zscale : ['function' | 'functionlog' | 'linear' | 'log' | 'logit' | 'symlog'], optional\n",
       "    The z scale.  Note that currently, only a linear scale is\n",
       "    supported.\n",
       "sharez : Axes3D, optional\n",
       "    Other axes to share z-limits with.\n",
       "proj_type : {'persp', 'ortho'}\n",
       "    The projection type, default 'persp'.\n",
       "\n",
       "Notes\n",
       "-----\n",
       ".. versionadded:: 1.2.1\n",
       "    The *sharez* parameter.\n",
       "\u001b[0;31mFile:\u001b[0m           ~/conda/envs/python/lib/python3.6/site-packages/mpl_toolkits/mplot3d/axes3d.py\n",
       "\u001b[0;31mType:\u001b[0m           type\n",
       "\u001b[0;31mSubclasses:\u001b[0m     \n"
      ]
     },
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    }
   ],
   "source": [
    "Axes3D?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m Clear the current figure.\n",
       "\u001b[0;31mFile:\u001b[0m      ~/conda/envs/python/lib/python3.6/site-packages/matplotlib/pyplot.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
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   "source": [
    "plt.clf?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcla\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m Clear the current axes.\n",
       "\u001b[0;31mFile:\u001b[0m      ~/conda/envs/python/lib/python3.6/site-packages/matplotlib/pyplot.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
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   "source": [
    "plt.cla?"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "k-means will partition your customers into mutually exclusive groups, for example, into 3 clusters. The customers in each cluster are similar to each other demographically.\n",
    "Now we can create a profile for each group, considering the common characteristics of each cluster. \n",
    "For example, the 3 clusters can be:\n",
    "\n",
    "- AFFLUENT, EDUCATED AND OLD AGED\n",
    "- MIDDLE AGED AND MIDDLE INCOME\n",
    "- YOUNG AND LOW INCOME"
   ]
  },
  {
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   "source": [
    "<h2>Want to learn more?</h2>\n",
    "\n",
    "IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"http://cocl.us/ML0101EN-SPSSModeler\">SPSS Modeler</a>\n",
    "\n",
    "Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at <a href=\"https://cocl.us/ML0101EN_DSX\">Watson Studio</a>\n",
    "\n",
    "<h3>Thanks for completing this lesson!</h3>\n",
    "\n",
    "<h4>Author:  <a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a></h4>\n",
    "<p><a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a>, PhD is a Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients’ ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.</p>\n",
    "\n",
    "<hr>\n",
    "\n",
    "<p>Copyright &copy; 2018 <a href=\"https://cocl.us/DX0108EN_CC\">Cognitive Class</a>. This notebook and its source code are released under the terms of the <a href=\"https://bigdatauniversity.com/mit-license/\">MIT License</a>.</p>"
   ]
  }
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